#import "X_var.typ": resultunusable

Reproducibility in computer science experiments becomes a requirement for research works.
Results can be confirmed by using the same datasets and methodologies can be compared by executing the software on different datasets. 
For researchers in the field of security of mobile platforms, these requirements are difficult to implement. 
Datasets of Android applications with well-known ground truth about what to expect in term of detection or information leakage for example may be too small or outdated. 
Additionally, the software produced by researchers seems to be difficult to re-use, which endanger both reproducibility of research and the proposal of new work built on top of previous works. 
This paper intends to explore the robustness of past software dedicated to static analysis of Android applications. 
We pursue the community effort that identified software supporting publications that perform static analysis of mobile applications and we propose a method for evaluating the reliability of these software. 
We extensively evaluate static analysis tools on a recent dataset of Android applications including goodware and malware, that we designed to measure the influence of  parameters such as the date and size of applications. 
Our results show that #resultunusable of the evaluated tools are no longer usable and that the size of the bytecode and the min SDK version have the greatest influence on the reliability of tested tools. 

